This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.

Knit to html for best performance.

Calculation is based on BONUS GOHERR project and its http://en.opasnet.org/w/Goherr_assessment.

What needs to be done for PFAS assessment?

  1. Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE
  2. Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
  3. Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
  4. The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution.
  5. Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
  6. Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
  7. Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
  8. Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
  9. Add dioxin and MeHg concentrations. Later.
  10. Now that the model runs technically, look through each part to check that it makes sense.
# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]

dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)

CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)

cat("Laskennassa käytetty data.\n")
## Laskennassa käytetty data.
dat
cat("Tarkastellut päätökset.\n")
## Tarkastellut päätökset.
dec
cat("Aggregoidut marginaalit.\n")
## Aggregoidut marginaalit.
CTable
dummy <- Ovariable("dummy",data=data.frame(Age="dummy", Result=1))

fish_proportion <- Ovariable( # How age groups eat fish differently
  "fish_proportion",
  dependencies = data.frame(Name="dummy"),
  formula = function(...) {
    out = prepare(dat,"fish_proportion",c("Type","Exposure_agent","Response","Unit"))
    out$Result <- as.numeric(as.character(out$Result))
    out$Result <- out$Result / sum(out$Result) * length(out$Result)
    return(out)
  },
  unit="-")

amount <- Ovariable(
  "amount",
  dependencies = data.frame(Name="fish_proportion"),
  formula = function(...) {
    amount = Ovariable("total_amount", data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")))
      # Filleted weight, i.e. no loss.
    amount <- amount * 1000 / 5.52 /365.25 
      # M kg/a per 5.52M population --> g/d per average person.
    amount <- amount * fish_proportion
      # fish_proportion tells the relative amount in each subgroup
  
    # Match KKE-classification in amount with Fineli classification
    tmp <- Ovariable(
      output = data.frame(
        Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
        Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
        Result = 1
      ),
      marginal = c(TRUE, TRUE, FALSE)
    )
    
    amount <- amount * tmp
    
    return(amount)
  },
  unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included

amount <- EvalOutput(amount)
## Loading required package: reshape2
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
ggplot(amount@output, aes(x=Age, weight=amountResult, fill=Kala))+geom_bar()+
  labs(
    title="Kalansyönti Suomessa ikäryhmittäin",
    y="Syönti (g/d)"
  )

ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")
## Saving 7 x 5 in image
population <- Ovariable(
  "population",
  data = prepare(dat,"population",c("Type","Exposure_agent","Response","Unit")),
  unit="#")

population <- Ovariable(
  "population", 
  data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
  unit = "#"
)

incidence <- Ovariable(
  "incidence",
  data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
  unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""

case_burden <- Ovariable(
  "case_burden",
  data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
  unit="DALY/case")

ERFchoice <- Ovariable(
  "ERFchoice",
  data = 
    prepare(dat, "ERFchoice", c("Unit", "Type"))
)

InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
  "InpBoD",
  data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
  unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"
conc_vit <- Ovariable(
  "conc_vit",
  ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
  subset = "Fineli data for common fish species"
)
  df = conc_vit@data
  df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
  df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
  df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
  df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
  df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
  df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
  df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df

######## Concentration of PFAS

# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
#       POP     mean       sd      min   Q0.025   median   Q0.975      max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935

# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
#      POP   mean       sd      min   Q0.025   median   Q0.975      max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994

conc_eukalat <- EvalOutput(Ovariable(
  "conc_eukalat",
  data = data.frame(
    Area = c("Suomi","Helsinki"),
    Compound="PFOS",
    Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
  unit="ng/g fresh weight"
))

conc_pfas <- Ovariable(
  "conc_pfas",
  data=opbase.data("Op_fi5932",subset="PFAS concentrations"),
  unit="ng/g fresh weight")
conc_pfas@data$Area <- "Porvoo"
conc_pfas <- EvalOutput(conc_pfas)

ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
  scale_x_log10()+
  stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
  scale_linetype_manual(values=c("dotted","solid","twodash"))+
  labs(
    title="PFAS concentration in fishes in Finland",
    x="PFAS concentration (ng/g fresh weight)",
    y="Cumulative probability"
  )

# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")
## Saving 7 x 5 in image
sum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
tmp <- conc_pfas / sum_pfas
summary(tmp, marginals="Compound")
## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of 
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.

conc <- Ovariable(
  "conc",
  dependencies = data.frame(Name="conc_vit", "conc_pfas"),
  formula = function(...){
    conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
    colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"

    conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean)
    conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
    conc_pfas <- oapply(conc_pfas, cols="", FUN=sum)
    
    out <- OpasnetUtils::combine(conc_vit, conc_pfas)
    return(out)
  }
)
conc <- EvalOutput(conc)

ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
  facet_wrap(~Compound, scales="free_x")

###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#

mc2dparam<- list(
  N2 = 10, # Number of iterations in the new Iter
  strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
  run2d = FALSE, # Should the mc2d function be used or not?
  info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
  newmarginals = c("Group","Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
  method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
  fun = mean # Function for aggregating the first Iter dimension.
)

if(FALSE) {
## Exposure with background exposure but without mother's exposure to child

expo_dir <- Ovariable(
  "expo_dir",
  dependencies=data.frame(Name=c("amount","conc","expo_bg")),
  formula = function(...) {
    out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
    out$Exposure_agent <- "Fish"
    out <- combine(conc, out, name="conc")
    out <- oapply(amount * out, cols="Fish", FUN=sum)
    out <- Ovariable(output = data.frame(
      Exposcen = c("BAU", "No exposure"),
      Result = c(1, 0)
    ), marginal=c(TRUE,FALSE)) * out + expo_bg
    out$Exposure <- as.factor(
      ifelse(
        out$Exposure_agent %in% c("DHA", "MeHg"),
        "To child",
        "To eater"
      )
    )
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)

## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
  "addexposure",
  ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
  subset = "Background exposure",
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)

# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
  "expo_bg",
  dependencies = data.frame(Name="addexposure","info"),
  formula = function(...) {
    out <- addexposure
    
    # Empty values ("") in indices must be replaced by NA so that Ops works correctly.
    levels(out$Gender)[levels(out$Gender) == ""] <- NA
    levels(out$Country)[levels(out$Country) == ""] <- NA
    levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
    out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
    
    temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
    temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
    temp1$Exposure_agent <- "TEQ"
    
    temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
    temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
    temp2$Exposure_agent <- "Omega3"
    
    out <- combine(out, temp1, temp2)
    out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
    
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)

# Stores non-marginal columns for further use.
info <- Ovariable(
  "info",
  dependencies = data.frame(Name = c("jsp")),
  formula = function(...) {
    out <- jsp
    out$Group <- factor(
      paste(out$Gender, out$Ages),
      levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
    )
    out$Country <- factor(out$Country, ordered=FALSE)
    out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
    out$Result <- 1
    return(out)
  }
)

} # END IF
############################### Code from Goherr assessment ends

expo_dir <- Ovariable(
  "expo_dir",
  dependencies = data.frame(Name=c("conc", "amount")),
  formula = function(...) {
    
    conc$Fish[conc$Fish %in% c("Perch","Pike-perch","Eel","")] <- "Average fish"
    conc <- oapply(conc, cols="", FUN=mean)
    out <- conc * amount 
    
#    colnames(out@output)[colnames(out@output)=="Nutrient"] <- "Exposure_agent"
      
    return(out)
  },
  unit="ng/d"
)

expo_dir <- EvalOutput(expo_dir)
#View(expo_dir@output)

ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
       aes(x=Age, weight=expo_dirResult,fill=Fish))+geom_bar()+
  facet_wrap(~Compound, scales="free_y")+
  labs(title="Eri yhdisteiden saanti kalasta")

ggsave("Yhdisteiden saanti kalasta Suomessa.svg")
## Saving 7 x 5 in image
exposure <- Ovariable(
  "exposure",
  dependencies = data.frame(
    Name = c(
      "expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
      "expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
      "mc2d" # 2D Monte Carlo function
    ),
    Ident = c(
      NA,
      "Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
      "Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
  ),
  formula = function(...) {
    out <- OpasnetUtils::combine(expo_dir, expo_indir)
    out <- unkeep(out, "Source.1", sources=TRUE)
    out <- mc2d(out)
    out$Exposure[is.na(out$Exposure)] <- "Direct"
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)

exposure <- Ovariable("exposure", data=data.frame(Result=1))
if(FALSE) {
exposure <- EvalOutput(exposure)
View(exposure@output)

ggplot(exposure@output, aes(x=Age, weight=exposureResult, fill=Fish))+geom_bar()+
  facet_grid(Compound~Exposure, scales="free_y")+
  labs(
    title="Exposure to compounds",
    y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
  )
} # END IF
objects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]
## Loading objects:
##   BoDattr
tryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))
##  Evaluating BoDattr ...
## 
##  - BoD fetched successfully!
## 
##  - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
## 
##  done(0 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
## 
##  done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
## 
##  done(0.01 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
## 
## - done(0.19 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
##  done!
## - Evaluating PAF ...
## 
##  - - dose fetched successfully!
## 
##  - - ERF fetched successfully!
## 
##  - - RR fetched successfully!
## 
##  - - frexposed fetched successfully!
## 
##  - - P_illness fetched successfully!
## 
##  - - sumExposcen fetched successfully!
## 
##  - - mc2d fetched successfully!
## - - Evaluating dose ...
## 
##  - - - BW fetched successfully!
## - - - Evaluating exposure ...
## 
##  done(0 secs)!
## - - - Checking exposure marginals ... exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ... done!
## - - - Evaluating BW ...
## 
##  done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
## 
## -- done(25.17 secs)!
## - - Checking dose marginals ... Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
## 
##  - - - ERF_env fetched successfully!
## 
##  - - - ERF_omega3 fetched successfully!
## 
##  - - - ERF_mehg fetched successfully!
## 
##  - - - ERF_diox fetched successfully!
## 
##  - - - ERF_vit fetched successfully!
## 
##  - - - ERF_micr fetched successfully!
## 
##  - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
## 
##  done(0.03 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
## 
##  done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
## 
##  done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
## 
##  done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
## 
##  done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
## 
##  done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
## 
##  done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
## 
##  done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
## 
## -- done(3.02 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
## 
## -- done(0.16 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
## 
##  done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
## 
##  done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
## 
## - done(6.6 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Age, Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
##  done!
## 
##  done(7.44 mins)!
##  Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, PAFSource, BoDattrSource recognized as marginal(s).
oprint(summary(amount,"mean"))
##               Kala Scenario              Age          Fish       mean
## 1      Kaupallinen      BAU     Female 18-45  Average fish  0.7187139
## 2       Muu tuonti      BAU     Female 18-45  Average fish  6.2887467
## 3       Vapaa-ajan      BAU     Female 18-45  Average fish  2.2235211
## 4      Kaupallinen      BAU       Female 45+  Average fish  1.7967848
## 5       Muu tuonti      BAU       Female 45+  Average fish 15.7218667
## 6       Vapaa-ajan      BAU       Female 45+  Average fish  5.5588029
## 7      Kaupallinen      BAU Non Female 18-45  Average fish  1.4374278
## 8       Muu tuonti      BAU Non Female 18-45  Average fish 12.5774934
## 9       Vapaa-ajan      BAU Non Female 18-45  Average fish  4.4470423
## 10     Kaupallinen      BAU   Non Female 45+  Average fish  2.3957130
## 11      Muu tuonti      BAU   Non Female 45+  Average fish 20.9624889
## 12      Vapaa-ajan      BAU   Non Female 45+  Average fish  7.4117372
## 13         Silakka      BAU     Female 18-45       Herring  0.3593570
## 14         Silakka      BAU       Female 45+       Herring  0.8983924
## 15         Silakka      BAU Non Female 18-45       Herring  0.7187139
## 16         Silakka      BAU   Non Female 45+       Herring  1.1978565
## 17       Kirjolohi      BAU     Female 18-45 Rainbow trout  1.5048072
## 18 Tuontikirjolohi      BAU     Female 18-45 Rainbow trout  1.0780709
## 19       Kirjolohi      BAU       Female 45+ Rainbow trout  3.7620181
## 20 Tuontikirjolohi      BAU       Female 45+ Rainbow trout  2.6951771
## 21       Kirjolohi      BAU Non Female 18-45 Rainbow trout  3.0096145
## 22 Tuontikirjolohi      BAU Non Female 18-45 Rainbow trout  2.1561417
## 23       Kirjolohi      BAU   Non Female 45+ Rainbow trout  5.0160241
## 24 Tuontikirjolohi      BAU   Non Female 45+ Rainbow trout  3.5935695
## 25      Tuontilohi      BAU     Female 18-45        Salmon  5.3005151
## 26      Tuontilohi      BAU       Female 45+        Salmon 13.2512876
## 27      Tuontilohi      BAU Non Female 18-45        Salmon 10.6010301
## 28      Tuontilohi      BAU   Non Female 45+        Salmon 17.6683835
## 29      Kasvatettu      BAU     Female 18-45     Whitefish  0.1347589
## 30      Kasvatettu      BAU       Female 45+     Whitefish  0.3368971
## 31      Kasvatettu      BAU Non Female 18-45     Whitefish  0.2695177
## 32      Kasvatettu      BAU   Non Female 45+     Whitefish  0.4491962
oprint(summary(BoD,"mean"))
##                                              Response       Age Gender Adjust
## 1                           Loss in child's IQ points     0 - 4 Female    BAU
## 2                                 Sperm concentration     0 - 4 Female    BAU
## 3                             Yes or no dental defect     0 - 4 Female    BAU
## 4                                 All-cause mortality     0 - 4 Female    BAU
## 5                                    Cancer morbidity     0 - 4 Female    BAU
## 6                                          Depression     0 - 4 Female    BAU
## 7                                      CHD2 mortality     0 - 4 Female    BAU
## 8                                   Immunosuppression     0 - 4 Female    BAU
## 9        Dioxin recommendation tolerable daily intake Undefined Female    BAU
## 10  Dioxin recommendation tolerable daily intake 2018 Undefined Female    BAU
## 11                                           PFAS TWI Undefined Female    BAU
## 12                           Vitamin D recommendation Undefined Female    BAU
## 13                                All-cause mortality     5 - 9 Female    BAU
## 14                                   Cancer morbidity     5 - 9 Female    BAU
## 15                                         Depression     5 - 9 Female    BAU
## 16                                     CHD2 mortality     5 - 9 Female    BAU
## 17                                  Immunosuppression     5 - 9 Female    BAU
## 18                                All-cause mortality   10 - 14 Female    BAU
## 19                                   Cancer morbidity   10 - 14 Female    BAU
## 20                                         Depression   10 - 14 Female    BAU
## 21                                     CHD2 mortality   10 - 14 Female    BAU
## 22                                All-cause mortality   15 - 19 Female    BAU
## 23                                   Cancer morbidity   15 - 19 Female    BAU
## 24                                         Depression   15 - 19 Female    BAU
## 25                                     CHD2 mortality   15 - 19 Female    BAU
## 26                                      Breast cancer   15 - 19 Female    BAU
## 27                                All-cause mortality   20 - 24 Female    BAU
## 28                                   Cancer morbidity   20 - 24 Female    BAU
## 29                                         Depression   20 - 24 Female    BAU
## 30                                     CHD2 mortality   20 - 24 Female    BAU
## 31                                      Breast cancer   20 - 24 Female    BAU
## 32                                All-cause mortality   25 - 29 Female    BAU
## 33                                   Cancer morbidity   25 - 29 Female    BAU
## 34                                         Depression   25 - 29 Female    BAU
## 35                                     CHD2 mortality   25 - 29 Female    BAU
## 36                                      Breast cancer   25 - 29 Female    BAU
## 37                                All-cause mortality   30 - 34 Female    BAU
## 38                                   Cancer morbidity   30 - 34 Female    BAU
## 39                                         Depression   30 - 34 Female    BAU
## 40                                     CHD2 mortality   30 - 34 Female    BAU
## 41                                      Breast cancer   30 - 34 Female    BAU
## 42                                All-cause mortality   35 - 39 Female    BAU
## 43                                   Cancer morbidity   35 - 39 Female    BAU
## 44                                         Depression   35 - 39 Female    BAU
## 45                                     CHD2 mortality   35 - 39 Female    BAU
## 46                                      Breast cancer   35 - 39 Female    BAU
## 47                                All-cause mortality   40 - 44 Female    BAU
## 48                                   Cancer morbidity   40 - 44 Female    BAU
## 49                                         Depression   40 - 44 Female    BAU
## 50                                     CHD2 mortality   40 - 44 Female    BAU
## 51                                      Breast cancer   40 - 44 Female    BAU
## 52                                All-cause mortality   45 - 49 Female    BAU
## 53                                   Cancer morbidity   45 - 49 Female    BAU
## 54                                         Depression   45 - 49 Female    BAU
## 55                                     CHD2 mortality   45 - 49 Female    BAU
## 56                                      Breast cancer   45 - 49 Female    BAU
## 57                                All-cause mortality   50 - 54 Female    BAU
## 58                                   Cancer morbidity   50 - 54 Female    BAU
## 59                                         Depression   50 - 54 Female    BAU
## 60                                     CHD2 mortality   50 - 54 Female    BAU
## 61                                      Breast cancer   50 - 54 Female    BAU
## 62                                All-cause mortality   55 - 59 Female    BAU
## 63                                   Cancer morbidity   55 - 59 Female    BAU
## 64                                         Depression   55 - 59 Female    BAU
## 65                                     CHD2 mortality   55 - 59 Female    BAU
## 66                                      Breast cancer   55 - 59 Female    BAU
## 67                                All-cause mortality   60 - 64 Female    BAU
## 68                                   Cancer morbidity   60 - 64 Female    BAU
## 69                                         Depression   60 - 64 Female    BAU
## 70                                     CHD2 mortality   60 - 64 Female    BAU
## 71                                      Breast cancer   60 - 64 Female    BAU
## 72                                All-cause mortality   65 - 69 Female    BAU
## 73                                   Cancer morbidity   65 - 69 Female    BAU
## 74                                         Depression   65 - 69 Female    BAU
## 75                                     CHD2 mortality   65 - 69 Female    BAU
## 76                                      Breast cancer   65 - 69 Female    BAU
## 77                                All-cause mortality   70 - 74 Female    BAU
## 78                                   Cancer morbidity   70 - 74 Female    BAU
## 79                                         Depression   70 - 74 Female    BAU
## 80                                     CHD2 mortality   70 - 74 Female    BAU
## 81                                      Breast cancer   70 - 74 Female    BAU
## 82                                All-cause mortality   75 - 79 Female    BAU
## 83                                   Cancer morbidity   75 - 79 Female    BAU
## 84                                         Depression   75 - 79 Female    BAU
## 85                                     CHD2 mortality   75 - 79 Female    BAU
## 86                                      Breast cancer   75 - 79 Female    BAU
## 87                                All-cause mortality   80 - 84 Female    BAU
## 88                                   Cancer morbidity   80 - 84 Female    BAU
## 89                                         Depression   80 - 84 Female    BAU
## 90                                     CHD2 mortality   80 - 84 Female    BAU
## 91                                      Breast cancer   80 - 84 Female    BAU
## 92                                All-cause mortality   85 - 89 Female    BAU
## 93                                   Cancer morbidity   85 - 89 Female    BAU
## 94                                         Depression   85 - 89 Female    BAU
## 95                                     CHD2 mortality   85 - 89 Female    BAU
## 96                                      Breast cancer   85 - 89 Female    BAU
## 97                                All-cause mortality   90 - 94 Female    BAU
## 98                                   Cancer morbidity   90 - 94 Female    BAU
## 99                                         Depression   90 - 94 Female    BAU
## 100                                    CHD2 mortality   90 - 94 Female    BAU
## 101                                     Breast cancer   90 - 94 Female    BAU
## 102                         Loss in child's IQ points     0 - 4   Male    BAU
## 103                               Sperm concentration     0 - 4   Male    BAU
## 104                           Yes or no dental defect     0 - 4   Male    BAU
## 105                               All-cause mortality     0 - 4   Male    BAU
## 106                                  Cancer morbidity     0 - 4   Male    BAU
## 107                                        Depression     0 - 4   Male    BAU
## 108                                    CHD2 mortality     0 - 4   Male    BAU
## 109                                 Immunosuppression     0 - 4   Male    BAU
## 110      Dioxin recommendation tolerable daily intake Undefined   Male    BAU
## 111 Dioxin recommendation tolerable daily intake 2018 Undefined   Male    BAU
## 112                                          PFAS TWI Undefined   Male    BAU
## 113                          Vitamin D recommendation Undefined   Male    BAU
## 114                               All-cause mortality     5 - 9   Male    BAU
## 115                                  Cancer morbidity     5 - 9   Male    BAU
## 116                                        Depression     5 - 9   Male    BAU
## 117                                    CHD2 mortality     5 - 9   Male    BAU
## 118                                 Immunosuppression     5 - 9   Male    BAU
## 119                               All-cause mortality   10 - 14   Male    BAU
## 120                                  Cancer morbidity   10 - 14   Male    BAU
## 121                                        Depression   10 - 14   Male    BAU
## 122                                    CHD2 mortality   10 - 14   Male    BAU
## 123                               All-cause mortality   15 - 19   Male    BAU
## 124                                  Cancer morbidity   15 - 19   Male    BAU
## 125                                        Depression   15 - 19   Male    BAU
## 126                                    CHD2 mortality   15 - 19   Male    BAU
## 127                                     Breast cancer   15 - 19   Male    BAU
## 128                               All-cause mortality   20 - 24   Male    BAU
## 129                                  Cancer morbidity   20 - 24   Male    BAU
## 130                                        Depression   20 - 24   Male    BAU
## 131                                    CHD2 mortality   20 - 24   Male    BAU
## 132                                     Breast cancer   20 - 24   Male    BAU
## 133                               All-cause mortality   25 - 29   Male    BAU
## 134                                  Cancer morbidity   25 - 29   Male    BAU
## 135                                        Depression   25 - 29   Male    BAU
## 136                                    CHD2 mortality   25 - 29   Male    BAU
## 137                                     Breast cancer   25 - 29   Male    BAU
## 138                               All-cause mortality   30 - 34   Male    BAU
## 139                                  Cancer morbidity   30 - 34   Male    BAU
## 140                                        Depression   30 - 34   Male    BAU
## 141                                    CHD2 mortality   30 - 34   Male    BAU
## 142                                     Breast cancer   30 - 34   Male    BAU
## 143                               All-cause mortality   35 - 39   Male    BAU
## 144                                  Cancer morbidity   35 - 39   Male    BAU
## 145                                        Depression   35 - 39   Male    BAU
## 146                                    CHD2 mortality   35 - 39   Male    BAU
## 147                                     Breast cancer   35 - 39   Male    BAU
## 148                               All-cause mortality   40 - 44   Male    BAU
## 149                                  Cancer morbidity   40 - 44   Male    BAU
## 150                                        Depression   40 - 44   Male    BAU
## 151                                    CHD2 mortality   40 - 44   Male    BAU
## 152                                     Breast cancer   40 - 44   Male    BAU
## 153                               All-cause mortality   45 - 49   Male    BAU
## 154                                  Cancer morbidity   45 - 49   Male    BAU
## 155                                        Depression   45 - 49   Male    BAU
## 156                                    CHD2 mortality   45 - 49   Male    BAU
## 157                                     Breast cancer   45 - 49   Male    BAU
## 158                               All-cause mortality   50 - 54   Male    BAU
## 159                                  Cancer morbidity   50 - 54   Male    BAU
## 160                                        Depression   50 - 54   Male    BAU
## 161                                    CHD2 mortality   50 - 54   Male    BAU
## 162                                     Breast cancer   50 - 54   Male    BAU
## 163                               All-cause mortality   55 - 59   Male    BAU
## 164                                  Cancer morbidity   55 - 59   Male    BAU
## 165                                        Depression   55 - 59   Male    BAU
## 166                                    CHD2 mortality   55 - 59   Male    BAU
## 167                                     Breast cancer   55 - 59   Male    BAU
## 168                               All-cause mortality   60 - 64   Male    BAU
## 169                                  Cancer morbidity   60 - 64   Male    BAU
## 170                                        Depression   60 - 64   Male    BAU
## 171                                    CHD2 mortality   60 - 64   Male    BAU
## 172                                     Breast cancer   60 - 64   Male    BAU
## 173                               All-cause mortality   65 - 69   Male    BAU
## 174                                  Cancer morbidity   65 - 69   Male    BAU
## 175                                        Depression   65 - 69   Male    BAU
## 176                                    CHD2 mortality   65 - 69   Male    BAU
## 177                                     Breast cancer   65 - 69   Male    BAU
## 178                               All-cause mortality   70 - 74   Male    BAU
## 179                                  Cancer morbidity   70 - 74   Male    BAU
## 180                                        Depression   70 - 74   Male    BAU
## 181                                    CHD2 mortality   70 - 74   Male    BAU
## 182                                     Breast cancer   70 - 74   Male    BAU
## 183                               All-cause mortality   75 - 79   Male    BAU
## 184                                  Cancer morbidity   75 - 79   Male    BAU
## 185                                        Depression   75 - 79   Male    BAU
## 186                                    CHD2 mortality   75 - 79   Male    BAU
## 187                                     Breast cancer   75 - 79   Male    BAU
## 188                               All-cause mortality   80 - 84   Male    BAU
## 189                                  Cancer morbidity   80 - 84   Male    BAU
## 190                                        Depression   80 - 84   Male    BAU
## 191                                    CHD2 mortality   80 - 84   Male    BAU
## 192                                     Breast cancer   80 - 84   Male    BAU
## 193                               All-cause mortality   85 - 89   Male    BAU
## 194                                  Cancer morbidity   85 - 89   Male    BAU
## 195                                        Depression   85 - 89   Male    BAU
## 196                                    CHD2 mortality   85 - 89   Male    BAU
## 197                                     Breast cancer   85 - 89   Male    BAU
## 198                               All-cause mortality   90 - 94   Male    BAU
## 199                                  Cancer morbidity   90 - 94   Male    BAU
## 200                                        Depression   90 - 94   Male    BAU
## 201                                    CHD2 mortality   90 - 94   Male    BAU
## 202                                     Breast cancer   90 - 94   Male    BAU
##           mean
## 1   16395.2448
## 2    4376.4000
## 3     336.1075
## 4    4367.2700
## 5     279.4400
## 6       0.4400
## 7      51.4100
## 8     272.0400
## 9     309.2078
## 10    899.5137
## 11   2810.9804
## 12    618.4157
## 13    909.3800
## 14    352.6300
## 15     87.5700
## 16     57.2800
## 17    248.0400
## 18   1012.2200
## 19    357.1200
## 20    680.2900
## 21     87.0900
## 22   1945.6100
## 23    386.2000
## 24   1657.4000
## 25    153.9800
## 26      3.0000
## 27   2684.3600
## 28    524.2800
## 29   2295.7100
## 30    228.8100
## 31     14.4000
## 32   2980.0200
## 33    728.7600
## 34   2185.6700
## 35    342.4600
## 36     72.4600
## 37   3569.3800
## 38   1173.6800
## 39   1876.2400
## 40    456.4700
## 41    289.2600
## 42   4372.6700
## 43   1750.8000
## 44   2009.3100
## 45    655.6500
## 46    525.1700
## 47   5831.2700
## 48   2660.2800
## 49   1980.0500
## 50   1017.7100
## 51    863.5600
## 52   8481.7500
## 53   4204.3800
## 54   1805.0000
## 55   1511.8900
## 56   1403.4500
## 57  13751.6900
## 58   7197.9400
## 59   2050.3500
## 60   2418.4000
## 61   2079.3300
## 62  19024.8900
## 63  10372.2500
## 64   2139.7100
## 65   3830.7900
## 66   2667.9900
## 67  25603.4700
## 68  14317.8700
## 69   2050.2600
## 70   6174.0400
## 71   3008.1400
## 72  34094.4500
## 73  18551.3000
## 74   1910.2400
## 75  10009.4600
## 76   3280.9700
## 77  44484.9600
## 78  21707.2100
## 79   1738.8100
## 80  16202.4200
## 81   3328.9100
## 82  41490.0000
## 83  15942.7400
## 84    992.1300
## 85  18996.6800
## 86   2448.5100
## 87  51345.4900
## 88  13735.8800
## 89    754.1900
## 90  28299.7900
## 91   1889.1800
## 92  49589.0500
## 93   8476.7900
## 94    508.9600
## 95  30471.0100
## 96   1151.1600
## 97  36317.0200
## 98   4083.6700
## 99    282.5900
## 100 23285.7800
## 101   589.9300
## 102 17161.5101
## 103  4580.9400
## 104   351.8162
## 105  5367.6600
## 106   339.6000
## 107     0.3900
## 108    55.8100
## 109   319.9300
## 110   301.6056
## 111   877.3982
## 112  2741.8694
## 113   603.2113
## 114   952.8700
## 115   338.4100
## 116    65.4000
## 117    40.2800
## 118   275.0300
## 119  1158.8700
## 120   337.0300
## 121   431.4800
## 122    63.5400
## 123  3986.2800
## 124   443.7500
## 125   984.2500
## 126   136.7100
## 127     1.1800
## 128  7865.3300
## 129   614.7700
## 130  1386.3200
## 131   279.3900
## 132     1.1700
## 133  9547.0600
## 134   862.1500
## 135  1417.2600
## 136   532.8300
## 137     1.1800
## 138  9851.2400
## 139  1122.2900
## 140  1303.8000
## 141   816.3700
## 142     1.0600
## 143 12178.6200
## 144  1607.4500
## 145  1434.1400
## 146  1680.8700
## 147     2.0300
## 148 14632.7600
## 149  2244.8500
## 150  1420.5100
## 151  2937.4200
## 152     3.1400
## 153 18900.8100
## 154  3494.3800
## 155  1263.3400
## 156  4722.7300
## 157     6.4600
## 158 28892.0400
## 159  6777.8600
## 160  1387.5600
## 161  8534.1800
## 162    11.3200
## 163 40698.9300
## 164 12102.4100
## 165  1394.6200
## 166 13664.1400
## 167    15.6400
## 168 52798.2800
## 169 18345.9500
## 170  1303.2200
## 171 19981.9000
## 172    18.2900
## 173 64496.5600
## 174 25248.0000
## 175  1212.9100
## 176 26192.0400
## 177    17.6900
## 178 75114.4400
## 179 29396.5100
## 180  1113.6600
## 181 32896.1500
## 182    27.1600
## 183 57367.0300
## 184 20087.7200
## 185   607.2700
## 186 27830.2900
## 187    15.5100
## 188 52592.3400
## 189 15565.9500
## 190   408.4000
## 191 27498.8800
## 192    12.5800
## 193 35941.6400
## 194  8349.3200
## 195   217.1900
## 196 20009.0600
## 197     5.2200
## 198 17196.2600
## 199  2990.6800
## 200    89.6400
## 201  9975.4500
## 202     2.4300
oprint(summary(BoDattr,"mean"))
##                                             Response       Age Gender Adjust
## 1                          Loss in child's IQ points     0 - 4 Female    BAU
## 2                          Loss in child's IQ points     0 - 4   Male    BAU
## 3                          Loss in child's IQ points     0 - 4 Female    BAU
## 4                          Loss in child's IQ points     0 - 4   Male    BAU
## 5                                Sperm concentration     0 - 4 Female    BAU
## 6                            Yes or no dental defect     0 - 4 Female    BAU
## 7       Dioxin recommendation tolerable daily intake Undefined Female    BAU
## 8  Dioxin recommendation tolerable daily intake 2018 Undefined Female    BAU
## 9                                Sperm concentration     0 - 4   Male    BAU
## 10                           Yes or no dental defect     0 - 4   Male    BAU
## 11      Dioxin recommendation tolerable daily intake Undefined   Male    BAU
## 12 Dioxin recommendation tolerable daily intake 2018 Undefined   Male    BAU
## 13                                          PFAS TWI Undefined Female    BAU
## 14                                          PFAS TWI Undefined   Male    BAU
## 15                          Vitamin D recommendation Undefined Female    BAU
## 16                          Vitamin D recommendation Undefined   Male    BAU
## 17                               All-cause mortality     0 - 4 Female    BAU
## 18                                        Depression     0 - 4 Female    BAU
## 19                               All-cause mortality     0 - 4   Male    BAU
## 20                                        Depression     0 - 4   Male    BAU
## 21                                    CHD2 mortality     0 - 4 Female    BAU
## 22                                    CHD2 mortality     0 - 4   Male    BAU
##    Exposure_agent          mean
## 1             DHA -1.788072e+01
## 2             DHA -1.871641e+01
## 3            MeHg  1.925616e+03
## 4            MeHg  2.015614e+03
## 5             TEQ  1.875600e+01
## 6             TEQ  1.043562e+01
## 7             TEQ -2.501773e+03
## 8             TEQ -1.911467e+03
## 9             TEQ  1.963260e+01
## 10            TEQ  1.092335e+01
## 11            TEQ -2.440264e+03
## 12            TEQ -1.864471e+03
## 13           PFAS  0.000000e+00
## 14           PFAS  0.000000e+00
## 15      Vitamin D  6.184157e+02
## 16      Vitamin D  6.032113e+02
## 17           Fish -9.294861e+00
## 18           Fish -2.336224e-03
## 19           Fish -1.142399e+01
## 20           Fish -2.070744e-03
## 21         Omega3 -1.820771e-01
## 22         Omega3 -1.976604e-01
oprint(summary(case_burden,"mean"))
##                                            Response        mean
## 1      Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3                         Loss in child's IQ points 0.110000000
## 4                                          PFAS TWI 0.001004988
## 5                               Sperm concentration 2.500000000
## 6                          Vitamin D recommendation 0.001004988
## 7                           Yes or no dental defect 0.060000000
oprint(summary(conc,"mean"))
##             Fish  Compound      mean
## 1   Average fish       ALA  0.690000
## 2          Bream       ALA  0.220000
## 3        Herring       ALA  1.740000
## 4           Pike       ALA  0.080000
## 5  Rainbow trout       ALA  4.810000
## 6          Roach       ALA  0.100000
## 7         Salmon       ALA  7.960000
## 8        Vendace       ALA  1.350000
## 9      Whitefish       ALA  2.220000
## 10  Average fish       DHA  2.540000
## 11         Bream       DHA  2.730000
## 12       Herring       DHA  5.860000
## 13          Pike       DHA  0.300000
## 14 Rainbow trout       DHA  7.570000
## 15         Roach       DHA  2.870000
## 16        Salmon       DHA  6.690000
## 17       Vendace       DHA  3.000000
## 18     Whitefish       DHA  3.940000
## 19  Average fish      Fish  1.000000
## 20         Bream      Fish  1.000000
## 21       Herring      Fish  1.000000
## 22          Pike      Fish  1.000000
## 23 Rainbow trout      Fish  1.000000
## 24         Roach      Fish  1.000000
## 25        Salmon      Fish  1.000000
## 26       Vendace      Fish  1.000000
## 27     Whitefish      Fish  1.000000
## 28  Average fish    Omega3  7.000000
## 29         Bream    Omega3  6.000000
## 30       Herring    Omega3 24.000000
## 31          Pike    Omega3  0.500000
## 32 Rainbow trout    Omega3 18.000000
## 33         Roach    Omega3  5.000000
## 34        Salmon    Omega3 23.000000
## 35       Vendace    Omega3 10.000000
## 36     Whitefish    Omega3 10.000000
## 37         Bream      PFAS  4.950000
## 38           Eel      PFAS  7.895000
## 39       Herring      PFAS  1.655000
## 40         Perch      PFAS  8.793125
## 41    Pike-perch      PFAS  2.595000
## 42  Average fish Vitamin D  0.105000
## 43         Bream Vitamin D  0.140000
## 44       Herring Vitamin D  0.156000
## 45          Pike Vitamin D  0.021000
## 46 Rainbow trout Vitamin D  0.051000
## 47         Roach Vitamin D  0.100000
## 48        Salmon Vitamin D  0.067000
## 49       Vendace Vitamin D  0.094000
## 50     Whitefish Vitamin D  0.144000
oprint(summary(dose,"mean"))
##   Scaling       mean
## 1      BW 0.01428571
## 2   Log10 0.00000000
## 3    None 1.00000000
oprint(summary(ERF,"mean"))
##    Exposure_agent                                          Response
## 1            Fish                               All-cause mortality
## 2          Omega3                                     Breast cancer
## 3            Fish                                        Depression
## 4             DHA                         Loss in child's IQ points
## 5             TEQ                               Sperm concentration
## 6             TEQ                           Yes or no dental defect
## 7          Omega3                                    CHD2 mortality
## 8       Vitamin D                          Vitamin D recommendation
## 9            MeHg                         Loss in child's IQ points
## 10           PFAS                                 Immunosuppression
## 11           PFAS                                          PFAS TWI
## 12            TEQ                                  Cancer morbidity
## 13            TEQ      Dioxin recommendation tolerable daily intake
## 14            TEQ Dioxin recommendation tolerable daily intake 2018
## 15           Fish                               All-cause mortality
## 16         Omega3                                     Breast cancer
## 17           Fish                                        Depression
## 18            DHA                         Loss in child's IQ points
## 19            TEQ                               Sperm concentration
## 20            TEQ                           Yes or no dental defect
## 21         Omega3                                    CHD2 mortality
## 22      Vitamin D                          Vitamin D recommendation
## 23           MeHg                         Loss in child's IQ points
## 24           PFAS                                 Immunosuppression
## 25           PFAS                                          PFAS TWI
## 26            TEQ                                  Cancer morbidity
## 27            TEQ      Dioxin recommendation tolerable daily intake
## 28            TEQ Dioxin recommendation tolerable daily intake 2018
##      ER_function Scaling Observation          mean
## 1             RR    None         ERF   0.997871700
## 2             RR    None         ERF   0.999487200
## 3             RR    None         ERF   0.994690400
## 4            ERS    None         ERF  -0.001300000
## 5            ERS    None         ERF   0.000060000
## 6            ERS    None         ERF   0.001390971
## 7  Relative Hill    None         ERF  -0.170000000
## 8           Step    None         ERF 100.000000000
## 9            ERS      BW         ERF   9.800000000
## 10           ERS      BW         ERF   0.022700000
## 11           TWI      BW         ERF   4.400000000
## 12           CSF      BW         ERF   0.000500000
## 13           TDI      BW         ERF   2.000000000
## 14           TDI      BW         ERF   0.288900000
## 15            RR    None   Threshold   0.000000000
## 16            RR    None   Threshold   0.000000000
## 17            RR    None   Threshold   0.000000000
## 18           ERS    None   Threshold   0.000000000
## 19           ERS    None   Threshold   0.000000000
## 20           ERS    None   Threshold   0.000000000
## 21 Relative Hill    None   Threshold  47.000000000
## 22          Step    None   Threshold  10.000000000
## 23           ERS      BW   Threshold   0.000000000
## 24           ERS      BW   Threshold   0.000000000
## 25           TWI      BW   Threshold   0.000000000
## 26           CSF      BW   Threshold   0.000000000
## 27           TDI      BW   Threshold   0.000000000
## 28           TDI      BW   Threshold   0.000000000
oprint(summary(expo_dir,"mean"))
##              Fish  Compound            Kala Scenario              Age
## 1       Whitefish       ALA      Kasvatettu      BAU     Female 18-45
## 2       Whitefish       DHA      Kasvatettu      BAU     Female 18-45
## 3       Whitefish      Fish      Kasvatettu      BAU     Female 18-45
## 4       Whitefish    Omega3      Kasvatettu      BAU     Female 18-45
## 5       Whitefish Vitamin D      Kasvatettu      BAU     Female 18-45
## 6    Average fish       ALA     Kaupallinen      BAU     Female 18-45
## 7    Average fish       DHA     Kaupallinen      BAU     Female 18-45
## 8    Average fish      Fish     Kaupallinen      BAU     Female 18-45
## 9    Average fish    Omega3     Kaupallinen      BAU     Female 18-45
## 10   Average fish      PFAS     Kaupallinen      BAU     Female 18-45
## 11   Average fish Vitamin D     Kaupallinen      BAU     Female 18-45
## 12  Rainbow trout       ALA       Kirjolohi      BAU     Female 18-45
## 13  Rainbow trout       DHA       Kirjolohi      BAU     Female 18-45
## 14  Rainbow trout      Fish       Kirjolohi      BAU     Female 18-45
## 15  Rainbow trout    Omega3       Kirjolohi      BAU     Female 18-45
## 16  Rainbow trout Vitamin D       Kirjolohi      BAU     Female 18-45
## 17   Average fish       ALA      Muu tuonti      BAU     Female 18-45
## 18   Average fish       DHA      Muu tuonti      BAU     Female 18-45
## 19   Average fish      Fish      Muu tuonti      BAU     Female 18-45
## 20   Average fish    Omega3      Muu tuonti      BAU     Female 18-45
## 21   Average fish      PFAS      Muu tuonti      BAU     Female 18-45
## 22   Average fish Vitamin D      Muu tuonti      BAU     Female 18-45
## 23        Herring       ALA         Silakka      BAU     Female 18-45
## 24        Herring       DHA         Silakka      BAU     Female 18-45
## 25        Herring      Fish         Silakka      BAU     Female 18-45
## 26        Herring    Omega3         Silakka      BAU     Female 18-45
## 27        Herring      PFAS         Silakka      BAU     Female 18-45
## 28        Herring Vitamin D         Silakka      BAU     Female 18-45
## 29  Rainbow trout       ALA Tuontikirjolohi      BAU     Female 18-45
## 30  Rainbow trout       DHA Tuontikirjolohi      BAU     Female 18-45
## 31  Rainbow trout      Fish Tuontikirjolohi      BAU     Female 18-45
## 32  Rainbow trout    Omega3 Tuontikirjolohi      BAU     Female 18-45
## 33  Rainbow trout Vitamin D Tuontikirjolohi      BAU     Female 18-45
## 34         Salmon       ALA      Tuontilohi      BAU     Female 18-45
## 35         Salmon       DHA      Tuontilohi      BAU     Female 18-45
## 36         Salmon      Fish      Tuontilohi      BAU     Female 18-45
## 37         Salmon    Omega3      Tuontilohi      BAU     Female 18-45
## 38         Salmon Vitamin D      Tuontilohi      BAU     Female 18-45
## 39   Average fish       ALA      Vapaa-ajan      BAU     Female 18-45
## 40   Average fish       DHA      Vapaa-ajan      BAU     Female 18-45
## 41   Average fish      Fish      Vapaa-ajan      BAU     Female 18-45
## 42   Average fish    Omega3      Vapaa-ajan      BAU     Female 18-45
## 43   Average fish      PFAS      Vapaa-ajan      BAU     Female 18-45
## 44   Average fish Vitamin D      Vapaa-ajan      BAU     Female 18-45
## 45      Whitefish       ALA      Kasvatettu      BAU       Female 45+
## 46      Whitefish       DHA      Kasvatettu      BAU       Female 45+
## 47      Whitefish      Fish      Kasvatettu      BAU       Female 45+
## 48      Whitefish    Omega3      Kasvatettu      BAU       Female 45+
## 49      Whitefish Vitamin D      Kasvatettu      BAU       Female 45+
## 50   Average fish       ALA     Kaupallinen      BAU       Female 45+
## 51   Average fish       DHA     Kaupallinen      BAU       Female 45+
## 52   Average fish      Fish     Kaupallinen      BAU       Female 45+
## 53   Average fish    Omega3     Kaupallinen      BAU       Female 45+
## 54   Average fish      PFAS     Kaupallinen      BAU       Female 45+
## 55   Average fish Vitamin D     Kaupallinen      BAU       Female 45+
## 56  Rainbow trout       ALA       Kirjolohi      BAU       Female 45+
## 57  Rainbow trout       DHA       Kirjolohi      BAU       Female 45+
## 58  Rainbow trout      Fish       Kirjolohi      BAU       Female 45+
## 59  Rainbow trout    Omega3       Kirjolohi      BAU       Female 45+
## 60  Rainbow trout Vitamin D       Kirjolohi      BAU       Female 45+
## 61   Average fish       ALA      Muu tuonti      BAU       Female 45+
## 62   Average fish       DHA      Muu tuonti      BAU       Female 45+
## 63   Average fish      Fish      Muu tuonti      BAU       Female 45+
## 64   Average fish    Omega3      Muu tuonti      BAU       Female 45+
## 65   Average fish      PFAS      Muu tuonti      BAU       Female 45+
## 66   Average fish Vitamin D      Muu tuonti      BAU       Female 45+
## 67        Herring       ALA         Silakka      BAU       Female 45+
## 68        Herring       DHA         Silakka      BAU       Female 45+
## 69        Herring      Fish         Silakka      BAU       Female 45+
## 70        Herring    Omega3         Silakka      BAU       Female 45+
## 71        Herring      PFAS         Silakka      BAU       Female 45+
## 72        Herring Vitamin D         Silakka      BAU       Female 45+
## 73  Rainbow trout       ALA Tuontikirjolohi      BAU       Female 45+
## 74  Rainbow trout       DHA Tuontikirjolohi      BAU       Female 45+
## 75  Rainbow trout      Fish Tuontikirjolohi      BAU       Female 45+
## 76  Rainbow trout    Omega3 Tuontikirjolohi      BAU       Female 45+
## 77  Rainbow trout Vitamin D Tuontikirjolohi      BAU       Female 45+
## 78         Salmon       ALA      Tuontilohi      BAU       Female 45+
## 79         Salmon       DHA      Tuontilohi      BAU       Female 45+
## 80         Salmon      Fish      Tuontilohi      BAU       Female 45+
## 81         Salmon    Omega3      Tuontilohi      BAU       Female 45+
## 82         Salmon Vitamin D      Tuontilohi      BAU       Female 45+
## 83   Average fish       ALA      Vapaa-ajan      BAU       Female 45+
## 84   Average fish       DHA      Vapaa-ajan      BAU       Female 45+
## 85   Average fish      Fish      Vapaa-ajan      BAU       Female 45+
## 86   Average fish    Omega3      Vapaa-ajan      BAU       Female 45+
## 87   Average fish      PFAS      Vapaa-ajan      BAU       Female 45+
## 88   Average fish Vitamin D      Vapaa-ajan      BAU       Female 45+
## 89      Whitefish       ALA      Kasvatettu      BAU Non Female 18-45
## 90      Whitefish       DHA      Kasvatettu      BAU Non Female 18-45
## 91      Whitefish      Fish      Kasvatettu      BAU Non Female 18-45
## 92      Whitefish    Omega3      Kasvatettu      BAU Non Female 18-45
## 93      Whitefish Vitamin D      Kasvatettu      BAU Non Female 18-45
## 94   Average fish       ALA     Kaupallinen      BAU Non Female 18-45
## 95   Average fish       DHA     Kaupallinen      BAU Non Female 18-45
## 96   Average fish      Fish     Kaupallinen      BAU Non Female 18-45
## 97   Average fish    Omega3     Kaupallinen      BAU Non Female 18-45
## 98   Average fish      PFAS     Kaupallinen      BAU Non Female 18-45
## 99   Average fish Vitamin D     Kaupallinen      BAU Non Female 18-45
## 100 Rainbow trout       ALA       Kirjolohi      BAU Non Female 18-45
## 101 Rainbow trout       DHA       Kirjolohi      BAU Non Female 18-45
## 102 Rainbow trout      Fish       Kirjolohi      BAU Non Female 18-45
## 103 Rainbow trout    Omega3       Kirjolohi      BAU Non Female 18-45
## 104 Rainbow trout Vitamin D       Kirjolohi      BAU Non Female 18-45
## 105  Average fish       ALA      Muu tuonti      BAU Non Female 18-45
## 106  Average fish       DHA      Muu tuonti      BAU Non Female 18-45
## 107  Average fish      Fish      Muu tuonti      BAU Non Female 18-45
## 108  Average fish    Omega3      Muu tuonti      BAU Non Female 18-45
## 109  Average fish      PFAS      Muu tuonti      BAU Non Female 18-45
## 110  Average fish Vitamin D      Muu tuonti      BAU Non Female 18-45
## 111       Herring       ALA         Silakka      BAU Non Female 18-45
## 112       Herring       DHA         Silakka      BAU Non Female 18-45
## 113       Herring      Fish         Silakka      BAU Non Female 18-45
## 114       Herring    Omega3         Silakka      BAU Non Female 18-45
## 115       Herring      PFAS         Silakka      BAU Non Female 18-45
## 116       Herring Vitamin D         Silakka      BAU Non Female 18-45
## 117 Rainbow trout       ALA Tuontikirjolohi      BAU Non Female 18-45
## 118 Rainbow trout       DHA Tuontikirjolohi      BAU Non Female 18-45
## 119 Rainbow trout      Fish Tuontikirjolohi      BAU Non Female 18-45
## 120 Rainbow trout    Omega3 Tuontikirjolohi      BAU Non Female 18-45
## 121 Rainbow trout Vitamin D Tuontikirjolohi      BAU Non Female 18-45
## 122        Salmon       ALA      Tuontilohi      BAU Non Female 18-45
## 123        Salmon       DHA      Tuontilohi      BAU Non Female 18-45
## 124        Salmon      Fish      Tuontilohi      BAU Non Female 18-45
## 125        Salmon    Omega3      Tuontilohi      BAU Non Female 18-45
## 126        Salmon Vitamin D      Tuontilohi      BAU Non Female 18-45
## 127  Average fish       ALA      Vapaa-ajan      BAU Non Female 18-45
## 128  Average fish       DHA      Vapaa-ajan      BAU Non Female 18-45
## 129  Average fish      Fish      Vapaa-ajan      BAU Non Female 18-45
## 130  Average fish    Omega3      Vapaa-ajan      BAU Non Female 18-45
## 131  Average fish      PFAS      Vapaa-ajan      BAU Non Female 18-45
## 132  Average fish Vitamin D      Vapaa-ajan      BAU Non Female 18-45
## 133     Whitefish       ALA      Kasvatettu      BAU   Non Female 45+
## 134     Whitefish       DHA      Kasvatettu      BAU   Non Female 45+
## 135     Whitefish      Fish      Kasvatettu      BAU   Non Female 45+
## 136     Whitefish    Omega3      Kasvatettu      BAU   Non Female 45+
## 137     Whitefish Vitamin D      Kasvatettu      BAU   Non Female 45+
## 138  Average fish       ALA     Kaupallinen      BAU   Non Female 45+
## 139  Average fish       DHA     Kaupallinen      BAU   Non Female 45+
## 140  Average fish      Fish     Kaupallinen      BAU   Non Female 45+
## 141  Average fish    Omega3     Kaupallinen      BAU   Non Female 45+
## 142  Average fish      PFAS     Kaupallinen      BAU   Non Female 45+
## 143  Average fish Vitamin D     Kaupallinen      BAU   Non Female 45+
## 144 Rainbow trout       ALA       Kirjolohi      BAU   Non Female 45+
## 145 Rainbow trout       DHA       Kirjolohi      BAU   Non Female 45+
## 146 Rainbow trout      Fish       Kirjolohi      BAU   Non Female 45+
## 147 Rainbow trout    Omega3       Kirjolohi      BAU   Non Female 45+
## 148 Rainbow trout Vitamin D       Kirjolohi      BAU   Non Female 45+
## 149  Average fish       ALA      Muu tuonti      BAU   Non Female 45+
## 150  Average fish       DHA      Muu tuonti      BAU   Non Female 45+
## 151  Average fish      Fish      Muu tuonti      BAU   Non Female 45+
## 152  Average fish    Omega3      Muu tuonti      BAU   Non Female 45+
## 153  Average fish      PFAS      Muu tuonti      BAU   Non Female 45+
## 154  Average fish Vitamin D      Muu tuonti      BAU   Non Female 45+
## 155       Herring       ALA         Silakka      BAU   Non Female 45+
## 156       Herring       DHA         Silakka      BAU   Non Female 45+
## 157       Herring      Fish         Silakka      BAU   Non Female 45+
## 158       Herring    Omega3         Silakka      BAU   Non Female 45+
## 159       Herring      PFAS         Silakka      BAU   Non Female 45+
## 160       Herring Vitamin D         Silakka      BAU   Non Female 45+
## 161 Rainbow trout       ALA Tuontikirjolohi      BAU   Non Female 45+
## 162 Rainbow trout       DHA Tuontikirjolohi      BAU   Non Female 45+
## 163 Rainbow trout      Fish Tuontikirjolohi      BAU   Non Female 45+
## 164 Rainbow trout    Omega3 Tuontikirjolohi      BAU   Non Female 45+
## 165 Rainbow trout Vitamin D Tuontikirjolohi      BAU   Non Female 45+
## 166        Salmon       ALA      Tuontilohi      BAU   Non Female 45+
## 167        Salmon       DHA      Tuontilohi      BAU   Non Female 45+
## 168        Salmon      Fish      Tuontilohi      BAU   Non Female 45+
## 169        Salmon    Omega3      Tuontilohi      BAU   Non Female 45+
## 170        Salmon Vitamin D      Tuontilohi      BAU   Non Female 45+
## 171  Average fish       ALA      Vapaa-ajan      BAU   Non Female 45+
## 172  Average fish       DHA      Vapaa-ajan      BAU   Non Female 45+
## 173  Average fish      Fish      Vapaa-ajan      BAU   Non Female 45+
## 174  Average fish    Omega3      Vapaa-ajan      BAU   Non Female 45+
## 175  Average fish      PFAS      Vapaa-ajan      BAU   Non Female 45+
## 176  Average fish Vitamin D      Vapaa-ajan      BAU   Non Female 45+
##             mean
## 1     0.29916466
## 2     0.53094990
## 3     0.13475886
## 4     1.34758857
## 5     0.01940528
## 6     0.49591260
## 7     1.82553332
## 8     0.71871391
## 9     5.03099734
## 10    4.61968337
## 11    0.07546496
## 12    7.23812283
## 13   11.39139082
## 14    1.50480724
## 15   27.08653035
## 16    0.07674517
## 17    4.33923521
## 18   15.97341657
## 19    6.28874668
## 20   44.02122677
## 21   40.42222945
## 22    0.66031840
## 23    0.62528110
## 24    2.10583175
## 25    0.35935695
## 26    8.62456688
## 27    0.59473576
## 28    0.05605968
## 29    5.18552083
## 30    8.16099641
## 31    1.07807086
## 32   19.40527547
## 33    0.05498161
## 34   42.19209987
## 35   35.46044575
## 36    5.30051506
## 37  121.91184637
## 38    0.35513451
## 39    1.53422959
## 40    5.64774372
## 41    2.22352115
## 42   15.56464804
## 43   14.29214541
## 44    0.23346972
## 45    0.74791166
## 46    1.32737475
## 47    0.33689714
## 48    3.36897144
## 49    0.04851319
## 50    1.23978149
## 51    4.56383331
## 52    1.79678477
## 53   12.57749336
## 54   11.54920841
## 55    0.18866240
## 56   18.09530708
## 57   28.47847705
## 58    3.76201810
## 59   67.71632587
## 60    0.19186292
## 61   10.84808802
## 62   39.93354142
## 63   15.72186670
## 64  110.05306692
## 65  101.05557362
## 66    1.65079600
## 67    1.56320275
## 68    5.26457936
## 69    0.89839238
## 70   21.56141719
## 71    1.48683939
## 72    0.14014921
## 73   12.96380209
## 74   20.40249102
## 75    2.69517715
## 76   48.51318868
## 77    0.13745403
## 78  105.48024969
## 79   88.65111437
## 80   13.25128765
## 81  304.77961593
## 82    0.88783627
## 83    3.83557398
## 84   14.11935929
## 85    5.55880287
## 86   38.91162009
## 87   35.73036353
## 88    0.58367430
## 89    0.59832933
## 90    1.06189980
## 91    0.26951771
## 92    2.69517715
## 93    0.03881055
## 94    0.99182519
## 95    3.65106664
## 96    1.43742781
## 97   10.06199469
## 98    9.23936673
## 99    0.15092992
## 100  14.47624566
## 101  22.78278164
## 102   3.00961448
## 103  54.17306069
## 104   0.15349034
## 105   8.67847042
## 106  31.94683314
## 107  12.57749336
## 108  88.04245353
## 109  80.84445889
## 110   1.32063680
## 111   1.25056220
## 112   4.21166349
## 113   0.71871391
## 114  17.24913375
## 115   1.18947152
## 116   0.11211937
## 117  10.37104167
## 118  16.32199281
## 119   2.15614172
## 120  38.81055094
## 121   0.10996323
## 122  84.38419975
## 123  70.92089150
## 124  10.60103012
## 125 243.82369274
## 126   0.71026902
## 127   3.06845918
## 128  11.29548743
## 129   4.44704230
## 130  31.12929607
## 131  28.58429082
## 132   0.46693944
## 133   0.99721555
## 134   1.76983299
## 135   0.44919619
## 136   4.49196191
## 137   0.06468425
## 138   1.65304198
## 139   6.08511107
## 140   2.39571302
## 141  16.76999115
## 142  15.39894455
## 143   0.25154987
## 144  24.12707611
## 145  37.97130273
## 146   5.01602414
## 147  90.28843449
## 148   0.25581723
## 149  14.46411737
## 150  53.24472190
## 151  20.96248894
## 152 146.73742255
## 153 134.74076482
## 154   2.20106134
## 155   2.08427033
## 156   7.01943915
## 157   1.19785651
## 158  28.74855626
## 159   1.98245253
## 160   0.18686562
## 161  17.28506945
## 162  27.20332136
## 163   3.59356953
## 164  64.68425157
## 165   0.18327205
## 166 140.64033291
## 167 118.20148583
## 168  17.66838353
## 169 406.37282123
## 170   1.18378170
## 171   5.11409864
## 172  18.82581239
## 173   7.41173716
## 174  51.88216012
## 175  47.64048471
## 176   0.77823240
#oprint(summary(exposure,"mean"))
oprint(summary(fish_proportion,"mean"))
##                Age      mean
## 1     Female 18-45 0.4528302
## 2       Female 45+ 1.1320755
## 3 Non Female 18-45 0.9056604
## 4   Non Female 45+ 1.5094340
oprint(summary(incidence,"mean"))
##                                            Response       Age Adjust   mean
## 1                         Loss in child's IQ points     0 - 4    BAU 1.1920
## 2                               Sperm concentration     0 - 4    BAU 0.0140
## 3                           Yes or no dental defect     0 - 4    BAU 0.0448
## 4      Dioxin recommendation tolerable daily intake Undefined    BAU 0.1100
## 5 Dioxin recommendation tolerable daily intake 2018 Undefined    BAU 0.3200
## 6                                          PFAS TWI Undefined    BAU 1.0000
## 7                          Vitamin D recommendation Undefined    BAU 0.2200
oprint(summary(PAF,"mean"))
##    Exposure_agent                                          Response       Age
## 1             DHA                         Loss in child's IQ points     0 - 4
## 2            MeHg                         Loss in child's IQ points     0 - 4
## 3             TEQ                               Sperm concentration     0 - 4
## 4             TEQ                           Yes or no dental defect     0 - 4
## 5            Fish                               All-cause mortality     0 - 4
## 6          Omega3                                     Breast cancer     0 - 4
## 7            Fish                                        Depression     0 - 4
## 8          Omega3                                    CHD2 mortality     0 - 4
## 9             TEQ      Dioxin recommendation tolerable daily intake Undefined
## 10            TEQ Dioxin recommendation tolerable daily intake 2018 Undefined
## 11      Vitamin D                          Vitamin D recommendation Undefined
## 12           PFAS                                          PFAS TWI Undefined
## 13           Fish                               All-cause mortality Undefined
## 14         Omega3                                     Breast cancer Undefined
## 15           Fish                                        Depression Undefined
## 16         Omega3                                    CHD2 mortality Undefined
##    Adjust         mean
## 1     BAU -0.001090604
## 2     BAU  0.117449664
## 3     BAU  0.004285714
## 4     BAU  0.031048457
## 5     BAU -0.002128300
## 6     BAU -0.000512800
## 7     BAU -0.005309600
## 8     BAU -0.003541667
## 9     BAU -8.090909091
## 10    BAU -2.125000000
## 11    BAU  1.000000000
## 12    BAU  0.000000000
## 13    BAU -0.002128300
## 14    BAU -0.000512800
## 15    BAU -0.005309600
## 16    BAU -0.003541667
oprint(summary(population,"mean"))
##    Gender       Age    mean
## 1  Female     0 - 4  125040
## 2    Male     0 - 4  130884
## 3  Female   10 - 14  151113
## 4    Male   10 - 14  157712
## 5  Female   15 - 19  144441
## 6    Male   15 - 19  152230
## 7  Female   20 - 24  152265
## 8    Male   20 - 24  161679
## 9  Female   25 - 29  172593
## 10   Male   25 - 29  183092
## 11 Female   30 - 34  169653
## 12   Male   30 - 34  181115
## 13 Female   35 - 39  174660
## 14   Male   35 - 39  186122
## 15 Female   40 - 44  168547
## 16   Male   40 - 44  177928
## 17 Female   45 - 49  154391
## 18   Male   45 - 49  159982
## 19 Female     5 - 9  149633
## 20   Male     5 - 9  156654
## 21 Female   50 - 54  176612
## 22   Male   50 - 54  179182
## 23 Female   55 - 59  185152
## 24   Male   55 - 59  183719
## 25 Female   60 - 64  183336
## 26   Male   60 - 64  176283
## 27 Female   65 - 69  185685
## 28   Male   65 - 69  171275
## 29 Female   70 - 74  186034
## 30   Male   70 - 74  163697
## 31 Female   75 - 79  118190
## 32   Male   75 - 79   93987
## 33 Female   80 - 84   96256
## 34   Male   80 - 84   65140
## 35 Female       85+  103429
## 36   Male       85+   47581
## 37 Female Undefined 2797030
## 38   Male Undefined 2728262
oprint(summary(RR,"mean"))
##   Exposure_agent            Response   ER_function Scaling      mean
## 1           Fish All-cause mortality            RR    None 0.9978717
## 2         Omega3       Breast cancer            RR    None 0.9994872
## 3           Fish          Depression            RR    None 0.9946904
## 4         Omega3      CHD2 mortality Relative Hill    None 0.9964583
###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)

plot_ly(trim(amount), x=~Scenario, y=~amountResult, color=~Kala, type="bar") %>%
  layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
  layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
tmp <- exposure / Ovariable(
  output = data.frame(
    Exposure_agent = c("Fish","Vitamin D", "Omega3", "ALA", "DHA", "TEQ", "PFAS"),
    Result = c(1, 1, 1000, 1000, 1000, 1, 1)
  ),
  marginal = c(TRUE, FALSE)
)

plot_ly(trim(tmp), x=~exposureSource, y=~Result, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
  layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))
cat("Kalaperäisiä tautitaakkoja Suomessa\n")
## Kalaperäisiä tautitaakkoja Suomessa
if(openv$N>1) {
  tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
  tmp <- data.frame(
    Altiste = tmp$Exposure_agent,
    Vaikutus = tmp$Response,
    Keskiarvo = as.character(signif(tmp$mean,2)),
    "95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
    Keskihajonta = signif(tmp$sd,2)
  )#[rev(match(lev, tmp$Exposure_agent)),]

  oprint(tmp)
  
  tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
  tmp <- data.frame(
    Terveysvaikutus = tmp$Response,
    Keskiarvo = signif(tmp$mean,2),
    "95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
    Keskihajonta = signif(tmp$sd,2)
  )
  oprint(tmp)
}

ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()

ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()

plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
  layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]
## Loading objects:
##   makeGraph
gr <- makeGraph(gr)
## Loading required package: DiagrammeR
## Loading objects:
##   formatted
## Loading objects:
##   chooseGr
#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")
## Loading objects:
##   showind
##   binoptest
##   showLoctable
##   ovashapetest
showLoctable()
showind()
## subgrouping is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.